docs: def sg_conv1d(tensor, opt):
      r"""Applies a 1-D convolution.
      
      Args:
        tensor: A 3-D `Tensor` (automatically passed by decorator).
        opt:
          size: A positive `integer` representing `[kernel width]`.
            If not specified, 2 is set implicitly.
          stride: A positive `integer`. The number of entries by which
            the filter is moved right at each step.
          in_dim: A positive `integer`. The size of input dimension.
          dim: A positive `integer`. The size of output dimension.
          pad: Either `SAME` (Default) or `VALID`.
          bias: Boolean. If True, biases are added.
          regularizer:  A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable
            will be added to the collection tf.GraphKeys.REGULARIZATION_LOSSES and can be used for regularization
          summary: If True, summaries are added. The default is True.
  
      Returns:
        A `Tensor` with the same type as `tensor`.
      """
      # default options
      opt += tf.sg_opt(size=2, stride=1, pad='SAME')
  
      # parameter tf.sg_initializer
      w = tf.sg_initializer.he_uniform('W', (opt.size, opt.in_dim, opt.dim),
                                       regularizer=opt.regularizer, summary=opt.summary)
      b = tf.sg_initializer.constant('b', opt.dim, summary=opt.summary) if opt.bias else 0
  
      # apply convolution
      out = tf.nn.conv1d(tensor, w, stride=opt.stride, padding=opt.pad) + b
  
      return out